Hybrid procurement model for the construction of library literature and information resource procurement
To improve the efficiency of intelligent procurement of library literature and intelligence resources, the study conducts the design of literature and intelligence resources procurement model. The procurement model is constructed by using the support vector machine, and the optimal parameters of the...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Systems and Soft Computing |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S277294192400053X |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | To improve the efficiency of intelligent procurement of library literature and intelligence resources, the study conducts the design of literature and intelligence resources procurement model. The procurement model is constructed by using the support vector machine, and the optimal parameters of the support vector machine are obtained by using the genetic algorithm. The experimental results demonstrated that the mean square error of the proposed model was only 0.03, which was 40 % lower compared with the procurement models based on other optimization algorithms. The average accuracy of the proposed model was as high as 95.18 % and the prediction accuracy was 95.78 % compared to other methods. The accuracy was improved by 15.11 %, 24.57 % and 19.67 % respectively compared to other models. The results show that using genetic algorithm to optimize support vector machine can effectively improve the prediction speed and prediction efficiency of the model. The proposed hybrid procurement model based on genetic algorithm and support vector machine can effectively meet the needs of library literature and intelligence resources procurement construction. The model has positive application significance in library literature and intelligence resources procurement. |
|---|---|
| ISSN: | 2772-9419 |